Internalizing disorders (i.e., anxiety and mood disorders) are common and debilitating conditions. The proposed Career Development Award will provide the candidate with the necessary skills to develop an independent research program focused on identifying how psychological/personality, environmental, and biomarker vulnerabilities predict the development and course of internalizing disorders in patient and population samples using prospective observational designs. The applicant's prior research training involved studying the influence of psychological/personality vulnerabilities on internalizing disorder severity and course in patient samples. In order to expand his research program, the applicant is seeking advanced training and experience in two key areas (psychiatric epidemiology and machine learning methods) and supplemental training in two additional areas (self-assessed biomarkers, environmental vulnerabilities). Collectively, this training will provide the applicant with the skills (1) to study internalizing disorder onset and course using prospective survey designs, and (2) to use machine learning methods to develop clinically-useful risk algorithms of disorder onset and subtypes of disorder course based on a multiple vulnerability domains (psychological/personality, environmental, and biomarkers). These skills will be developed through a combination of didactic training, guided readings, and mentored research projects. The proposed research plan involves two phases. In Phase 1, machine learning methods will be used to analyze cross-sectional internalizing disorder data from the World Mental Health (WMH) Surveys in order to develop risk algorithms that predict the onset of major depression, bipolar disorder, and generalized anxiety disorder following a stressful life event (Specific Aim 1). Machine learning methods will also be applied to WMH Survey data to develop subtypes of bipolar disorder, generalized anxiety disorder, and posttraumatic stress disorder that maximize the prediction of their long-term course (Specific Aim 2). The proposed Phase 1 studies are highly innovative; in contrast to other areas of medicine, virtually no studies have attempted to develop internalizing disorder risk algorithms or subtypes using machine learning methods. However, Phase 1 studies are preliminary; replication is needed in prospective samples. Accordingly, Phase 2 of the research plan aims (Specific Aim 3) to test the feasibility of conducting a large prospective web-based survey study of psychological/ personality, environmental, and biomarker vulnerabilities of internalizing disorder onset and course (i.e., self-report surveys of psychological/personality and environmental vulnerabilities; home-based biomarker collection via salivary assays and portable electronic devices). Participants will be recruited online to complete a baseline survey and those determined to have an internalizing disorder or be at high-risk of developing internalizing disorders will be asked to complete bimonthly follow-ups over the course of a year. The Phase 2 feasibility study is also innovative. Despite being called the future of epidemiological research web-based survey studies of the internalizing disorders are rare. In addition, existing prospective epidemiological studies of internalizing disorders tend to have very long gaps between follow-up assessments (1-4 years; increasing recall bias). The proposed bimonthly online survey assessments address this limitation. Overall, the outlined training activities and mentored research projects will be used to develop an R01 application for a prospective epidemiological study that uses machine learning methods to develop clinically useful algorithms of internalizing disorder onset and course using a broad range of psychological/personality, environmental, and biomarker vulnerabilities factors.